PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Christopher Williams

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Number of EPrints submitted by this user: 22

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video
Michalis Titsias and Christopher Williams
In: Generative-Model Based Vision 2004, 2 July 2004, Washington DC, USA.

Using the Equivalent Kernel to Understand Gaussian Process Regression
Peter Sollich and Christopher Williams
In: Neural Information Processing Systems 2004 (NIPS 2004), 14-16 Dec 2004, Vancouver, Canada.

Learning Sprites
Christopher Williams
In: Pattern Recognition and Machine Learning in Computer Vision Workshop, 3-5 May 2004, Grenoble, France.

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video
Christopher Williams
In: Sheffield Machine Learning Workshop, 7-10 Septmber 2004, Sheffield, England.

Consistency of Gaussian Process Prediction
Christopher Williams
In: Notions of Complexity: Information-theoretic, Computational and Statistical Approaches Workshop, 7-9 October 2004, Eindhoven, The Netherlands.

Approximate Methods for GP Regression: A Survey and an Empirical Comparison
Christopher Williams
In: Gaussian Process Roundtable, 9-10 June 2005, Sheffield, UK.

Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher Williams
(2006) Adaptive Computation and Machine Learning . MIT Press , Cambridge, Massachusetts, USA . ISBN 026218253X

The PASCAL Visual Object Classes Challenge 2006 (VOC 2006) Results
Mark Everingham, Andrew Zisserman, Christopher Williams and Luc Van Gool
(2006) Mark Everingham, Oxford, UK.

Predictive Search Distributions
Edwin Bonilla, Christopher Williams, Felix Agakov, John Cavazos, John Thompson and Michael F. P. O'Boyle
In: 23rd International Conference on Machine Learning, Pittsburgh, USA(2006).

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care
Christopher Williams and John Quinn
In: Neural Information Processing Systems, 5-8 December, 2005, Vancouver, Canada.

On a Connection between Object Localization with a Generative Template of Features and Pose-space Prediction Methods
Christopher Williams and Moray Allan
(2006) School of Informatics, University of Edinburgh, Edinburgh, UK.

Kernel Multi-task Learning using Task-specific Features
Edwin Bonilla, Felix Agakov and Christopher Williams
In: Eleventh International Conference on Artificial Intelligence and Statistics, 21-24 Mar 2007, San Juan, Puerto Rico.

Known Unknowns: Novelty Detection in Condition Monitoring
John Quinn and Christopher Williams
In: 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), 6-8 Jun 2007, Girona, Spain.

Multi-task Gaussian Process Prediction
Edwin Bonilla, Kian Ming Chai and Christopher Williams
In: Neural Information Processing Systems (NIPS) 2007, 3-6 Dec 2007, Vancouver, BC, Canada.

A Tutorial Introduction to Stochastic Differential Equations: Continuous-time Gaussian Markov Processes
Christopher Williams
In: Dynamical Systems, Stochastic Processes and Bayesian Inference, 9 Dec 2006, Whistler, BC, Canada.

Signal Masking in Gaussian Channels
John A. Quinn and Christopher Williams
In: ICASSP 2008, 30 Mar - 4 April 2008, Las Vegas, USA.

Learning generative texture models with extended Fields-of-Experts
Nicolas Heess, Christopher Williams and Geoffrey Hinton
In: Brtish Machine Vision Conference 2009, 7-10 Sept 2009, London, UK.

Object localisation using the Generative Template of Features
Moray Allan and Christopher Williams
Computer Vision and Image Understanding Volume 113, Number 7, pp. 824-838, 2009. ISSN 1077-3142

Advances in Neural Information Processing Systems 22
Yoshua Bengio, Dale Schuurmans, John Lafferty, Christopher Williams and Aron Culotta, ed. (2009) Neural Information Processing Systems Foundation .

Kick-starting GPLVM Optimization via a Connection to Metric MDS
Sebastian Bitzer and Christopher Williams
In: NIPS 2010 Workshop on Challenges of Data Visualization, 11 Dec 2010, Whistler, British Columbia, Canada.

Physiological Monitoring with Factorial Switching Linear Dynamical Systems
John Quinn and Christopher Williams
In: Bayesian Time Series Models (2011) Cambridge University Press , Cambridge, UK . ISBN 0521196760

Advances in Neural Information Processing Systems 23
John Lafferty, Christopher Williams, John Shawe-Taylor, Richard S. Zemel and Aron Culotta, ed. (2010) NIPS Foundation .